GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI

Lucas Ondel; Léa-Marie Lam-Yee-Mui; Martin Kocour; Caio Filippo Corro; Lukás Burget

ICASSP 2022accepted0 citations

GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI

Lucas Ondel, Léa-Marie Lam-Yee-Mui, Martin Kocour, Caio Filippo Corro, Lukás Burget

Abstract

We propose to express the forward-backward algorithm in terms of operations between sparse matrices in a specific semiring. This new perspective naturally leads to a GPU-friendly algorithm which is easy to implement in Julia or any programming languages with native support of semiring algebra. We use this new implementation to train a TDNN with the LF-MMI objective function and we compare the training time of our system with PyChain—a recently introduced C++/CUDA implementation of the LF-MMI loss. Our implementation is about two times faster while not having to use any approximation such as the "leaky-HMM".

BibTeX

@inproceedings{icassp2022_gpuacceleratedfo,
  title = {GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI},
  author = {Lucas Ondel and Léa-Marie Lam-Yee-Mui and Martin Kocour and Caio Filippo Corro and Lukás Burget},
  booktitle = {ICASSP 2022},
  year = {2022}
}